Alex Rasmussen examines some lessons learned while building record-setting sorting systems at UC San Diego.
John Oliver takes a look at both G1 and Shenandoah, explaining how they work, what are their limitations, providing tuning advice. He also looks at recent and future changes to garbage collection.
Monica Beckwith talks about G1 pause (young and mixed) composition, G1's remembered sets and collection set and G1's concurrent marking algorithm, providing performance tuning advice.
Chris Newland discusses performance-boosting techniques used by the JVM’s JIT and introduces JITWatch, a tool helping to get the best JVM performance for a code.
Brendan Gregg focuses on broken tools and metrics instead of the working ones. Metrics can be misleading, and counters can be counter-intuitive. He advises on how to approach new performance tools.
Nik Molnar discusses how to use client and server side profiling tools to improve the performance of a web application, providing solutions to the most common performance problems.
Rick Hudson discusses the motivation, performance, and technical challenges of Go's low latency concurrent GC and why the approach fits Go well.
Emad Benjamin covers various GC tuning techniques and how to best build platform engineered systems; in particular the focus is on tuning large scale JVM deployments.
Scott Seighman discusses causes of common performance issues in Big Data environments, heap size, garbage collection, JVM reuse tuning guidelines and Big Data performance analysis tools.
David Richardson presents the optimization techniques employed to set three world speed records using a combination of code generation and hardware specific optimizations.
Sebastian Zarnekow discusses JVM internal optimizations, presenting how the JVM sees through code to apply techniques like inlining, loop unrolling and escape analysis at runtime.
Gil Tene provides an overview of JIT compiler optimization techniques and their impact on common market-open slowdown scenarios.